bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Laminar organization of encoding and memory
reactivation in the parietal cortex
Aaron A. Wilber1,4*, Ivan Skelin2,3,4*, and Bruce L. McNaughton2,3
Egocentric neural coding has been observed in parietal cortex (PC), but its topographical and
laminar organization is not well characterized. We used multi-site recording to look for evidence of local clustering and laminar consistency of linear and angular velocity encoding in
multi-neuronal spiking activity (MUA) and in the high-frequency (300-900 Hz) component of
the local field potential (HF-LFP), believed to reflect local spiking activity. Rats were trained to
run many trials on a large circular platform, either to LED-cued goal locations or as a spatial
sequence from memory. Tuning to specific self-motion states was observed consistently, and
exhibited distinct cortical depth-invariant coding properties. These patterns of collective local
and laminar activation during behavior were reactivated in compressed form during post-experience sleep, and temporally coupled to hippocampal sharp wave ripples. Thus, PC neuron
motion encoding is consistent across cortical laminae, and this consistency is maintained
during memory reactivation.
Keywords: parietal cortex, posterior parietal cortex, modular organization, memory reactivation,
multi-unit activity, high frequency local field potential, template matching.
Highlights:
• Parietal cortex MUA encodes specific movements coherently across laminae.
• This organizational scheme is maintained during subsequent memory reactivation
• MUA and HF-LFP showed similar self-motion tuning and memory reactivation dynamics
• This establishes the utility of MUA and HF-LFP for human memory reactivation studies
INTRODUCTION
A fundamental framework for neural coding in the
parietal cortex is egocentric (e.g., Andersen et al.,
1985; McNaughton et al., 1994; Nitz, 2006; Save et al.,
2005; Save and Poucet, 2000; Schindler and Bartels,
2013; Whitlock et al., 2012; Wilber et al., 2014; Wolbers et al., 2008). In addition, some studies have also
found evidence for allocentric (world-centered) encoding in parietal cortex (Chen et al., 1994a, b; Chen
and Nakamura, 1998; Wilber et al., 2014). Subjective
assessment of head direction tuning in parietal cortex
suggested common tuning across depth on a given
tetrode (Chen et al., 1994a, b; Wilber et al., 2014). The
existence of larger organizational structure of these
single cells (e.g., large scale populations of cells) has
been postulated by the theoretical or computational
approaches (e.g., Byrne et al., 2007; McNaughton et al.,
1995; Xing and Andersen, 2000; Zipser and Andersen,
1988), but has not been empirically confirmed. Therefore, we set-out to look for evidence of population level
organization of three types of previously reported egocentric and allocentric reference frames, body centered
cue direction (Wilber et al., 2014), self-motion tuning
(McNaughton et al., 1994; Whitlock et al., 2012), and
head direction tuning (Chen et al., 1994a, b; Wilber et
al., 2014).
Behaviorally relevant neural activity patterns
from single cells are reactivated during memory consolidation (Dupret et al., 2010; Lansink and Pennartz,
2014). Thus, we hypothesized that the functional rel-
1Department of Psychology, Program in Neuroscience, Florida State University, Tallahassee, FL, 32308 USA
2Canadian Centre for Behavioural Neuroscience, The University of Lethbridge, Lethbridge, AB, T1K 3M4 Canada
3Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
4Contributed Equally
*Correspondence: [email protected] and [email protected]
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
evance of modular organizational structure would be
supported if the modular structure is also reflected in
reactivation during post-experience-sleep – a postulated mechanism of memory consolidation originally
demonstrated at the single cell level in hippocampus
(Wilson and McNaughton, 1994) and subsequently
shown for hippocampal-parietal interactions (Qin et
al., 1997). There is now evidence that memory replay
is tightly linked to plasticity at the level of single cells
(Tavoni et al., 2015; van de Ven et al.; Yang et al., 2014).
However, to our knowledge no study has looked for
evidence of modular level reactivation.
Assessing the large scale population-level
activity in animals would be useful for establishing the
connection between animal microelectrode recordings
and methods typically used for human neuroimaging
electrocorticography (ECoG) and functional magnetic resonance imaging (fMRI), whose spatial and/or
temporal resolution is usually limited to assessment
of compound activity of large populations of cells. An
increasing number of human ECoG studies provides
a wide cortical coverage, but lack single neuron separability. Therefore, linking spiking activity with local
field potential (LFP) features has been a growing area
of research, most often finding the correlation between
the spiking levels and LFP envelope in high-gamma
range (Crone et al., 2011; Liu and Newsome, 2006; Ray
and Maunsell, 2011); however, the high-gamma range
includes synaptic current oscillations (Colgin et al.,
2009; Fries, 2005) and not just spike-related currents.
Therefore, we aimed to test whether the modular organization reflected in MUA is also detectable in high
frequency LFP (>300 Hz), which to our knowledge
has not been attributed any functional or physiological
correlates other than spiking. The HF-LFP signal provides better temporal stability, relative to single neuron
recording due to minor electrode drift, and therefore
produces a more reliable readout for long-term studies
and for brain machine interfaces (e.g., Gilja et al., 2012;
Mazzoni et al., 2012). Similarly, recently an apparent
connection between single unit recording studies in
rodents (grid cells in rats; Hafting et al., 2005) to fMRI
data in humans (Constantinescu et al., 2016; Doeller
et al., 2010; Kriegeskorte and Storrs, 2016; Kunz et al.,
2015), suggest that collective recordings may reveal a
larger organizational structure of encoding that had
largely been described at the single cell level in rodents
(i.e., 6-fold symmetry initially observed in human
fMRI studies during virtual navigation tasks). Thus,
study of modularity of neural coding in animals using
collective measures of local neural activity can help to
bridge the gap between human and animal studies of
neural coding and dynamics.
METHODS
Male Fisher-Brown Norway hybrid rats (n=6), 5-10
months of age, underwent surgery for implantation
of bilateral stimulating electrodes aimed at the medial
forebrain bundle (MFB; 2.8 mm posterior from bregma, 1.7 mm from midline, 7.8 mm ventral from dura).
Prior to surgery rats were housed 2-3 per cage. After
recovery from surgery, rats were trained to nose poke
for MFB stimulation. Then brain stimulation parameters (200 μs half cycle, 150 Hz biphasic 70-110 μA
current applied for 300-450 ms) were adjusted to find
the minimal intensity and duration that was sufficient
for maintaining maximal responding. Next, rats with
optimal MFB stimulation (N=3 of the 6 with stimulating electrodes) underwent surgery to implant a
custom 18-tetrode bilateral “hyperdrive” or 18-tetrode
unilateral hyperdrive aimed at the left parietal cortex
(n=3; similar to: Kloosterman et al., 2009; Nguyen et
al., 2009).
Controls for MFB stimulation effects. MFB stimulation was necessary to obtain sufficient trials for some
analyses. To ameliorate concerns about MFB effects
on parietal cortex neural activity, data were removed
for the brain stimulation duration plus an additional
post-stimulation 20 ms blackout period (n=1; identical
to: Kloosterman et al., 2009; Nguyen et al., 2009). The
minimum post-stimulation blackout period of 20 ms
was only applied for self-motion analyses, for memory replay analyses a longer (500 ms) blackout period
applied because this was the time between trials when
the cue light remained off for all trial and task types.
In addition, MFB stimulation was delivered in one
hemisphere and recordings were obtained from both
hemispheres from most rats (n=2 of 3). For these rats
we compared the proportion of tetrodes (pooled across
sessions) that met the criteria for self-motion tuning in
the same versus opposite hemisphere to brain stimulation. Similar to our previous report of no effect on
proportion of any single cell types we measured (Bower et al., 2005; Euston and McNaughton, 2006; Euston
et al., 2007; Johnson et al., 2010; Wilber et al., 2014),
there were no differences in proportion of signifi-
Laminar organization of encoding and memory reactivation in the parietal cortex
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
cantly self-motion tuned sessions (p<0.01) between
hemispheres when self-motion tuning was measured
with MUA (χ2(1)=2.29, p=0.13) and HF LFP signal
(χ2(1)=0.28, p=0.60). Further, in previous experiments
where it was possible to obtain sufficient coverage
for analyses using food reward, identical results were
obtained using MFB stimulation and food reward,
suggesting that the results obtained from MFB experiments are generalizable (Euston and McNaughton,
2006). This suggests MFB stimulation did not directly
influence the activity patterns we observed.
Surgical Procedure. Recording arrays consisted of 18
tetrodes and 3-4 reference electrodes. Each tetrode
consisted of four polyimide-coated, nichrome wires
(12.5μm diameter) twisted together (Kanthal Palm
Coast). The recording arrays were positioned over
the parietal cortex (centered 4.5mm posterior from
bregma and +/-2.95mm from midline) arranged in
1 or 2 closely packed guide-tube bundles with center-to-center tetrode spacing of ~300 μm. The arrays
were positioned to target the average parietal cortex
region for which we recently thoroughly characterized
the connection densities (Mesina et al., 2016; Wilber
et al., 2015) and also simultaneously record from the
CA1 field of the hippocampus. These recording coordinates are likely to correspond to the intersection of the
mouse anteriormedial, posteriormedial, and mediomedial areas (Wang and Burkhalter, 2007; Wang et al.,
2012)
Behavior. Training and testing took place on a large
(1.5 m diameter) circular platform with 32 light cues
evenly distributed around the perimeter (similar
to the 8-station task; Bower et al., 2005). A custom
computer program (interfaced with the maze via a
Rat #
Task Type
field-programmable gate arrays (FPGA card, National
Instruments) controlled maze events, delivered MFB
stimulation rewards via a Stimulus Isolator (World
Precision Instruments A365) when the rat entered a 10
cm diameter zone in front of the active cue light, and
generated a coded timestamp in the Neuralynx system
for each maze event (e.g., light onset for a particular
zone). First, alternation training was achieved using
barriers to restrict the movement of the rat to alternating between a pair of cue lights on opposite sides of
the maze. To ensure cues were visually salient, lights
were flashed at ~3 Hz (with equal on/off time) when
activated. The first light was activated until the rat
reached that reward zone and received MFB stimulation and then the cue light in the opposite reward zone
was activated. Alternation training continued until
rats reached asymptote performance (rat 1 = 27 days
and rat 2 = 9 days; rat 3 = 20 days). The data reported
here for rats #1 and #2 comes from the next, random
lights, task, in which sequences of up to 900 elements
were drawn randomly with replacement from the 32
light/reward zones, with each LED remaining active
until the rat reached it. For rats #1 and #2, 25 recording sessions and 23 recording sessions were obtained,
respectively. A different random sequence was used
for each behavioral session to ensure that learning
effects did not contributing to the activity patterns.
The data reported here for rat #3 comes from the last,
spatial sequence, task that followed training on the
random lights task. For the spatial sequence task, rats
were trained to navigate through an 8-item repeating
element sequence of reward zones (Fig. 1). This task
consisted of 3 traversals through the sequence with cue
lights illuminated immediately (cued) and 3 travers-
Light/Dark Cycle Phase
During Testing
Number of Recording
Sessions
1
Up to 900-item fully random
Light
25
2
Up to 900-item fully random
Light
23
3
Spatial Sequence Task
Dark
16
Table 1. Training and Testing Conditions
Recording sessions refers to daily sessions that were typically split into two 50 min behavioral
sessions separated by a 50 min rest session.
Laminar organization of encoding and memory reactivation in the parietal cortex
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
als with a 5s delay (delay-cued), thus demonstrating
sequence memory (Bower et al., 2005). For delay-cued
trials, trained rats completed the trial before the appearance of light cues ≥ 90% of the time on average.
Rats #1-2 were housed in a vivarium with lights
on between 07:30 and 19:30 and were tested during
the light cycle. Rat #3 was housed in a reverse light/
dark cycle vivarium and tested during the dark cycle.
For rat #3, prominent distal cues were arranged around
the perimeter of a large room (~4.5 m x 6 m). For the
remaining rats, prominent cues (strips of white curtain) were displayed on 1-2 walls of a square curtain
that hung ~1m beyond the edge of the apparatus. For
all rats and sessions, dim ambient light illuminated the
maze from above. All experiments were carried out
in accordance with protocols approved by the University of Lethbridge Animal Welfare Committee and
conformed to NIH Guidelines on the Care and Use of
Laboratory Animals.
Recording Procedures. A custom electrode interface
board attached to the recording array with independently drivable tetrodes connected 3 unity-gain
headstages (HS-27, Neuralynx) to the recording
system (Neuralynx). Tetrodes were referenced to an
electrode in the corpus callosum and advanced as
needed, up to 60μm/day, while monitoring the audio
and visual signal of the unit activity, but adjustments
were carried out only after a given day’s recording to
allow overnight stabilization. Once a large number of
units in the parietal cortex were obtained, alternation
training commenced. Thresholded (adjusted prior to
each session) spike waveforms were filtered 0.6 to 6
kHz and digitized at 32 kHz. A continuous trace was
simultaneously collected from one of the tetrode wires
(filtered 0.1 to 9 kHz, and digitized at 2034.75 Hz and
referenced to an electrode in corpus callosum) for processing as a local field potential (LFP) and timestamps
were collected for up to 18 tetrodes. Rat position and
head direction were tracked using colored domes of
reflective tape, which were created by covering ½ Styrofoam balls with reflective tape (Fig. 1), and on-line
position information was used to trigger MFB stimulation rewards. Position and head direction data were
collected at 60Hz as interleaved video (rats 1 & 3) or
30Hz (rat 2) and co-registered with spikes, LFPs, and
stimuli.
Spike data were automatically overclustered
using KlustaKwik then manually adjusted using a
modified version of MClust (A.D. Redish). All spike
waveforms with a shape and across tetrode cluster
signature suggesting that they were likely multi-unit
activity (MUA) and not noise, were manually selected
and merged into a single MUA cluster. Thus, MUA
clusters included both well-isolated single units and
Figure 1. Apparatus, reference frames, and learned motion sequence
Left. Apparatus. Rats #1 and #2 were trained to run a random spatial sequence to 32 light locations. This task requires the
rat to learn to execute a stereotyped motion sequence, and covers the full range of headings and body-centered cue light
directions at a wide range of spatial locations. Middle. Single segment from the random lights task (red) overlaid on 99%
transparent subset (1/5th) of the trials from that session (blue). Note, that in the later training phases the route to the goal
becomes a series of stereotyped motion sequences. Right. Schematic of the spatial sequence task. The rat starts at zone
12 and continued to zone 28 8 20 4 28 8 20 & 12. Rat #3 learned to execute this spatial sequence from memory (without
cueing).
Laminar organization of encoding and memory reactivation in the parietal cortex
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
poorly isolated single units. MUA clusters were not
treated as unique unless a) the tetrode was moved
>100 μm from the previous session and b) the distributions of spike clusters were clearly unrelated between
the two sessions (http://klustakwik.sourceforge.net;
Harris et al., 2000).
LFP analyses were performed using custom-written Matlab code (Mathworks, Nattick, MA)
or Freely Moving Animal (FMA) Toolbox (http://fmatoolbox.sourceforge.net/). The LFP signal was collected
at 2034.75Hz and subsequently resampled to 2000 Hz
for further analysis, using the Matlab resample function. The amplitude of the high frequency (HF) LFP
signal was obtained by digitally filtering the raw signal
in the 300-900 Hz range using a 4-th order zero-phase
Chebyshev filter and calculating the absolute value of
the Hilbert transformed filtered signal.
Parietal Cortex Data. Data recorded from a subset of
the sessions (47 sessions) included MUA clusters that
were assessed because the tetrode had moved at least
100 μm from the previous session. Data from these
47 sessions (rat #1 = 14 sessions, rat #2 = 18 sessions,
rat #3 = 15 sessions) included 176 potentially unique
MUA clusters (number of putative unique MUA clusters for rat #1 = 46, rat #2 = 55, and rat #3 = 75). Each
recording session (except for 12 sessions of the 47 total
sessions presented here) consisted of three 50 min
rest sessions intermixed with two 50 min behavioral
sessions on the apparatus. The remaining 12 sessions
consisted of one behavioral session between two rest
sessions, followed by a 4h break during which the animal was returned to the vivarium before returning for
a second round of a 50 min behavioral session between
two more 50 min rest sessions. For these 12 sessions
only 1 of the rest-task-rest sessions was analyzed for a
given day.
Self-Motion Analyses. Position and head direction data
were utilized to map the self-motion reference frame
for each MUA cluster. For these analyses, position
data were interpolated and smoothed by convolution
with a Hamming window that was 1s long. In addition, head direction data gaps <1s were transformed
using directional cosines for interpolation using the
interp1 function in Matlab, then transformed back to
polar coordinates (Gumiaux et al., 2003). Next, head
angular velocity, linear velocity and MUA activity rate
were calculated for each video frame using a 100ms
sliding window. Finally, the occupancy and number of
MUA spikes for each 3 cm/s by 20°/s bin were calculated and converted to firing rate for each bin with
>0.5 s of occupancy. For illustrative purposes (not cell
classification analyses), the self-motion activity rate
maps were smoothed by convolving with a Gaussian
function for the 2 x 2 bins surrounding each bin (Chen
et al., 1994a). The self-motion colormaps represented
a range of MUA (or HF) activity rates from 0 (blue) to
the maximum (maroon). No adjustments were made
to the standard, evenly spaced colormap. MUA clusters were classified as having a preferred-self motion
state if the common points with sufficient occupancy
(i.e., >0.5 s) from the self-motion maps for the first
and second daily session (or split ½) were significantly
positively correlated (p<0.01). This was generally the
most conservative criterion for self-motion cells of the
three criteria reported by Whitlock et al. (Whitlock et
al., 2012). Specifically, for each MUA cluster, to determine if cells had “significant” self-motion properties,
the map from the first daily behavioral session was
shuffled, a correlation coefficient was computed between the first session (shuffled map) and the second
session (unshuffled map), and this process was repeated 500 times. Then, the second behavioral session map
was shuffled, the correlation coefficient was computed
between the second session (shuffled map) and the first
behavioral session (unshuffled map), and this process
was repeated 500 times (total 1000 shuffles/cell). The
entire shuffled data set for each cell was used to calculate a critical r-value for the 99th percentile. For the
day recording sessions with two behavioral sessions,
stability was assessed by comparing across the two
sessions and for the five recording sessions where a
single behavioral epoch was available, split ½ measures
of stability were used (i.e., a single session was split in
½ based on time and the first ½ was compared to the
second ½).
Memory Replay Analyses.
First, still periods were extracted from the rest
sessions as described previously (Euston et al., 2007;
Johnson et al., 2010). The raw position data from
each video frame was smoothed by convolution of
both x and y position data with a normalized Gaussian function with standard deviation of 120 video
frames. After smoothing, the instantaneous velocity
was found by taking the difference in position between
successive video frames. An epoch during which the
Laminar organization of encoding and memory reactivation in the parietal cortex
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
velocity dropped below 0.78 pixels/s for more than
2 minutes was judged to be a period of motionlessness. All analyses of rest sessions were limited to these
motionless periods. Slow wave sleep (SWS) and rapid
eye movement (REM) sleep were distinguished using
automatic K-means clustering of the theta/delta power ratio extracted from the CA1 pyramidal layer LFP
recorded during the ‘stillness’ periods (Girardeau et
al., 2009). Sharp-wave ripples (SWR) were detected
from the CA1 LFP digitally filtered in the 75-300 Hz
range. Events with peaks > 5 standard deviation (SD)
above the mean and duration less than 100 ms were
considered SWRs. The SWR duration included the
contiguous periods surrounding the peak and exceeding 2 SD above the mean. The SWR detection accuracy
was visually validated on the subset of each analyzed
dataset.
Template Matching Memory Replay Analyses.
We performed template matching analysis, previously
used to show the simultaneous reactivation of isolated single neuron ensembles in the medial prefrontal
cortex (Euston et al., 2007; Louie and Wilson, 2001a).
The criteria for the inclusion of a dataset in the template matching analysis was at least 12 min of SWS and
600 SWRs during both pre- and post-task-sleep, and at
least 50 trials during the task. Template matrices (number of tetrodes x number of time bins; Fig.2A) were
generated from the trial-averaged multiunit activity
(MUA template) or Hilbert-transformed high frequency amplitude (HF template), extracted from a 2 s/trial
windows that preceded the arrival at the reward site,
and binned to 100 ms bins. Only trials longer than the
template duration (2 s) were included. The time window was chosen based on evidence that reactivation of
the hippocampal activity patterns is more prominent
for the task phase immediately preceding the reward
(Diba and Buzsaki, 2007; Foster and Wilson, 2006; McNamara et al., 2014; Singer and Frank, 2009). The time
bin choice was based on previous reports of template
matching using isolated single unit neuron activity
(Euston et al., 2007; Johnson et al., 2010), where a 100
ms bin was deemed optimal for capturing task-related
neuronal dynamics. In order to eliminate tetrodes with
sparse and/or poorly approach-motion-modulated activity, only the tetrodes with average MUA > 1 Hz and
binned spike train coefficient of variation > 0.25 during
reward approach period were included in the template,
which resulted in elimination of 0-17% of tetrodes.
Only the templates containing 6 or more tetrodes were
retained in the analysis. To allow comparison between
the MUA and HF template matching, all the HF templates were constructed from the same set of tetrodes
that passed the MUA-based screening. To eliminate
the influence of the MUA or HF amplitude variability
between tetrodes, the binned signal was Z-scored for
each tetrode separately.
MUA and HF amplitude from the sleep periods
was processed in the same way, except that the bin size
was adjusted according to the compression factor (bin
size = 100 ms / compression factor; e.g. for the 4x compression, the sleep bin size was 25 ms), to capture the
compressed nature of neural reactivation during sleep.
An evenly spaced range of compression factors (4x, 6x,
8x, 10x), as well as the ‘no-compression’ (nc) control
were used. Only slow wave sleep periods were included
in the analysis.
To test the matching of a given template and the
pattern of activity during sleep (matching significance),
each template was shuffled repeatedly to generate 100
shuffled templates. The shuffling procedure consisted
of randomly permuting the position of each column
in the template (population vector), preserving the
overall activity levels and instantaneous correlations
between the MUA or HF signals on different tetrodes,
but scrambling the sequential patterns. A Pearson correlation coefficient was calculated between each template and the series of candidate matches, generated by
sliding the template-size window over the sleep epoch
(Fig. 2B). This resulted in a matrix of Pearson correlation coefficients r, where the element ri,j corresponded
to the correlation coefficient between the i-th template
and j-th candidate match. The correlation matrices
were Z-scored across individual time bins (columns),
and the resulting Z-score values reflected the template
similarity to the corresponding sleep segment at given
time step, relative to the distribution that included the
original and 100 shuffled templates. Z-score values
above 3 were considered matches. The threshold for
matching significance was that the number of matches
from the original template had to exceed the number
of matches from all of the 100 shuffled templates for a
given sleep epoch (i.e., p < 0.001).
For comparison of template matching between
the pre- and post-task-sleep, match percentage was
obtained by dividing the number of matches by the
number of SWS time bins for each sleep epoch. For the
Laminar organization of encoding and memory reactivation in the parietal cortex
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Figure 2. Template matching method.
A. Shuffling procedure. Above. Example original template. Multiunit activity (MUA) or high-frequency (HF) amplitude on each tetrode (row) is averaged over all the trials, binned at 100 ms and Z-scored. Below. Template
shuffling procedure. Position of each column (instantaneous MUA or HF amplitude over all the tetrodes) was
randomly permuted, in order to produce 100 shuffled templates while preserving the overall MUA or HF levels,
as well as the instantaneous correlation between tetrodes.
B. Template matching procedure. Above. MUA data segment from post-task sleep. Note, firing during sleep is
sparse, so samples are more variable. Activity on each tetrode (row) was binned at a bin size of 100 ms / compression factor and Z-scored. Below Left. Example match window from the sleep epoch (above) and template
(below). Pearson correlation coefficient was calculated between the template and equally sized slow-wave
sleep (SWS) segments of the sleep session, which were produced by sliding the template-size window over
the sleep epoch with a 1-bin step size. This creates the correlation matrix (number of templates x number of
time bins during sleep epoch). Each column of the correlation matrix is Z-scored, the Z-score value reflecting
the degree of similarity of a given template to sleep activity at given sleep time window, relative to the distribution across the original and all the shuffled templates. Bottom right: Example original template matching trace.
Z-score values above 3 are considered matches.
Laminar organization of encoding and memory reactivation in the parietal cortex
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
ripple-triggered average analysis, the original template
Z-score traces +/- 1 s around each SWR peak time
were averaged, obtaining the ripple-triggered averaged
Z-score for a given sleep epoch.
Statistical Analyses. Two-way repeated measures
ANOVA (both variables are repeated measures) was
used to assess the main effect of sleep session (pre- versus post- task sleep), the main effect of compression
factor (nc, 4x, 6x, 8x, and 10x), and interactions (sleep
session x compression factor) on template matching
measures. Significant interactions were followed up
by planned comparisons (F-tests) comparing pre-task
versus post-task-sleep for each compression factor.
Except where noted otherwise, p < 0.05 was considered
statistically significant. For example, one exception was
for self-motion map correlations, for these analyses we
used the historical critical value of 0.01 (Whitlock et
al., 2012; Wilber et al., 2014).
Histology. After the final recording session, rats were
deeply anesthetized with Euthasol and transcardially
perfused with 0.1M phosphate buffered saline followed
by 4% paraformaldehyde. The whole head was postfixed in 4% paraformaldehyde with electrodes in place
for 24h, then brains were extracted and cryoprotected
in 30% sucrose. Frozen sections were cut (40μm) using
a sliding microtome or custom block-face imaging
system (Leica vibratome), mounted on chrome alum-subbed slides, stained with cresyl-echt violet, and
imaged using a NanoZoomer Imaging system (Hamamatsu).
RESULTS
Results from MUA and HF-LFP analyses were
largely similar. Therefore, we report the MUA results
here and provide the corresponding HF-LFP data and
some comparisons in Supplemental materials.
Modular Tuning to Motion State. We looked for
evidence of population level encoding in egocentric
coordinates in the rat parietal cortex. To do this, we assessed MUA recorded on single tetrodes as a function
of either head direction, egocentric cue light location,
or linear, and angular velocity in rats that had been
trained to either run 1) the cued random spatial sequence (rats 1 & 2) or 2) a complex repeating element
spatial sequence from memory (rat 3). In contrast to
our previous single unit analysis we did not find evidence for MUA tuning to head direction (Chen et al.,
1994a; Wilber et al., 2014), and we did not find evidence for MUA tuning to egocentric cue light location
(not shown). It is possible the lack of head direction
tuning for MUA but presence with single cells means
that the head direction tuned cortical columns are
present but are very narrow and thus not detected with
MUA. Next, we determined if parietal cortex MUA had
a preferred self-motion state (e.g., right turn) by plotting firing rate as a function of the rat’s current linear
and angular velocity. MUA was classified as having a
preferred self-motion state if the self-motion rate maps
for two behavioral sessions were significantly positively correlated (i.e., self-motion rate maps; Chen et al.,
1994a; Whitlock et al., 2012; Wilber et al., 2014). We
found that the MUA recorded on a single tetrode was
frequently significantly tuned to a specific self-motion
state (i.e., specific combination of linear and angular
velocity). In fact, nearly every tetrode was significantly
tuned for at least one recording depth (41 of 44 tetrodes; 93%) of the tetrodes that were positioned in
parietal cortex for at least one session.
Interestingly, the tuning appeared to be invariant across depth. To quantify this observation, we
collected all of the pairs of sessions where the tetrode
was at least 100 μm from the comparison location and
the self-motion maps for both sessions were significant
(p<0.01). For each of these pairs we tested the two
maps for significant similarity versus a random shuffle
distribution using the same method as for within-day
behavioral sessions. Interestingly, self-motion tuning appeared to be consistent across cortical laminae
because tuning was consistent on a single tetrode when
recordings were compared even for very large spacing
between recording depths (Fig. 3B). In fact, a large
majority of comparisons were significant (Fig. 3C;
73%), indicating significant correlation in modular
self-motion tuning across a range of cortical depths
for a particular tetrode. In addition, as a control we
performed depth correlations across tetrodes (instead
of within tetrode) in the same manner (i.e., we took
each pair of sessions that was separated by at least 100
μm) and found significantly more correlations with an
r-value < 0 (χ2(1)=5.09, p<0.05) and significantly fewer
significant cross-tetrode map correlations (χ2(1)=5.68,
p<0.05). As an additional control we compared the
separation of significant and non-significant depth
spacing for comparison pairs. The distribution of
Laminar organization of encoding and memory reactivation in the parietal cortex
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
60
Session 2
r = 0.75
30
Linear Velocity (cm/s)
60
200
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Angular Velocity (°/s)
700μm
-200
Linear Velocity (cm/s)
C.
30
60
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4
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200
Angular Velocity (°/s)
B.
99th %ile
of the shuffled
distribution =
0.12
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Frequency
Session1
1400μm
0
200
r = 0.67
30
-200
0
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Angular Velocity (°/s)
Self-Motion Depth Pair Number
Linear Velocity (cm/s)
A.
-0.1
80
0
0.1
Correlation (r)
Similarity Across Depth
p < 0.01
ns
60
40
20
-0.2
0
0.2 0.4 0.6
Map Correlations (r-value)
Figure 3. Self-Motion tuning in parietal cortex is invariant across cortical laminae. See also Figure S1.
A. Left Two Plots. Multi-unit activity recorded for a single day’s recording session and from a single tetrode were classified
as having a preferred-self motion state if the self-motion maps for two behavioral sessions (from the whole day recording
session) were significantly positively correlated. Self-motion maps from two behavioral sessions and corresponding correlation value are shown for one multi-unit module. Occupancy data and number of spikes are binned according to linear
velocity (vertical axis) and head angular velocity (horizontal axis; positive head angular velocity corresponds to a right
turn), then converted to activity rate (number of multi-unit spikes per second). Right. The shuffled distribution and critical
r-value corresponding to the 99th percentile is shown. The map from the first behavioral session was shuffled 500 times
and a correlation coefficient was computed between the shuffled and unshuffled maps. Then, this process was repeated
to shuffle the map for the second session 500 times (total 1000 shuffles) in order to calculate a critical r-value for the 99th
percentile (p<0.01). See Materials and Methods for additional details.
B. Same as in A; however, data came from two separate recording sessions obtained when the tetrode was at two different depths (700μm, above and 1400μm, below). Black outline on lower motion rate map illustrates that for cross-depth
comparisons behavior can vary considerably, and this analysis is limited to common data points.
C. The sorted correlation value for each MUA cluster for each pair of depths where the tetrode was moved at least 100
μm and the session data for each depth met the significance criteria described in A. Pairs of depths with significantly correlated motion maps (as described in B, p<0.01) were colored red indicating significant correlation in modular self-motion
tuning across a range of cortical depths for a particular tetrode. Data comes from all tetrodes that met this criteria from all
3 rats.
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
depths for significant versus non-significant depth
correlations was highly overlapping: significant Mean
± SEM = 280 μm ± 25 and non-significant Mean ± SD
= 241 μm ± 31. There was no significant difference in
the difference in depths between the two samples for
significant depth correlations (t(72)=0.85, p=0.40), suggesting that closer spacing was not responsible for the
significant depth correlations.
Finally, if we substituted the amplitude of the
HF-LFP envelope (300-900Hz) for MUA firing rate,
similar tuning maps were observed and were often significantly correlated with the corresponding MUA map
(Fig. S1), consistent with the theory that the HF-LFP
signal reflects neuronal spiking. Further, when we took
each data set with significant within-session MUA motion map stability and compared the correlation values
for single recording session (behavioral session 1 vs
behavioral session 2 maps) obtained using HF-LFP
signal to the correlation values obtained when generating the same maps with MUA, we found that these
correlation values are significantly correlated (r=0.29,
p<0.01). It should be noted that although correspondence can be quite good for these two measures and
often subjectively appears very impressive (as shown in
Fig. S1), during behavior at least, the HF signal tends
to produce poorer tuning on average. We quantified
this slight difference by taking all of the self-motion
maps that were ‘significant’ at two different thresholds
Figure 4. Illustration of patchy modularity of MUA behavioral correlates in parietal cortex. Rows A and B represent
behavioral correlate maps for three nearby electrodes in left and right hemispheres respectively from rat 1. Row 3 is from
the left hemisphere of rat 2. The relative positions of the electrodes are shown on the right, where the minimum distance
represented is about 300 μm. In Row A, the correlates change abruptly from position to position, whereas in the Row B,
the correlates are quite similar from position to position. Row C illustrates a combination of these effects. As shown in
Figure 3, the correlates were highly consistent in the laminar dimension for each location. For illustration, the session
with the most significant behavior map was selected for each electrode. Thus, sessions were different for the examples
shown in A and B (and as a result the map shape varied), but was the same for the examples shown in C.
Laminar organization of encoding and memory reactivation in the parietal cortex
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
(p<0.05 and p<0.01) and comparing the number of
instances where a self-motion map is significant at
p<0.05 (but not p<0.01) versus significant at the higher
p<0.01 level for HF vs MUA. Significantly more MUA
data sets were significant at the higher (p<0.01) level
(χ2(1)=5.03, p<0.05).
Patchy, modular topographical organization of motion
correlates. In contrast to the high degree of laminar
consistency of behavioral correlates, the topographic
organization was patchy and modular: sometimes, a
tuning pattern was consistent across several near-by
sites, but there were also abrupt transitions between
some neighboring sites. Illustrations of this organization are provided in Figure 4.
Modular Interactions During Sleep. The random
lights task required the animals to execute a series
of actions in a relatively stereotyped manner (i.e., a
motion sequence), something the rats learn to do over
the course of training (see Fig. 1 for an example of a
path that has become stereotyped). Thus, we set out to
explore the possibility that this learned behavior may
be reflected at the neural level in the form of memory
reactivation involving self-motion modules during
sleep periods.
Template Matching
Parietal modular activation patterns during slow wave
sleep resemble patterns observed during the task. All
the template matching analysis was limited to SWS periods. First, we compared modular parietal activation
patterns from the task to post-task-sleep. We analyzed
results from 15 data sets (5 data sets from each rat),
selected based on distribution across training, numbers of trials (>50), presence of sufficient quantity of
slow-wave sleep (> 12 min / sleep epoch), number
of sharp wave ripples (>600 / sleep epoch) and number of tetrodes in parietal cortex that met criteria for
inclusion in the template (>5). A majority of these
data, 12 data sets, had 3 sleep epochs (pre-, mid- and
post- task-sleep interleaved with two task sessions)
while the remaining 3 data sets had two sleep epochs
(pre- & post- task-sleep with 1 task session). Consistent with previous reports, we found that memory
reactivation was more consistently stronger for the
post-task-sleep session in data sets with two task and
three sleep epochs (Euston et al., 2007; Johnson et al.,
2010). Therefore, we restricted our analysis to these
data sets (12 sets of pre-task-sleep, task, and post-
task-sleep from 3 rats). Due to the time-compressed
nature of re-activation reported previously for single
cell memory reactivation studies (Euston et al., 2007;
Peyrache et al., 2015; Wilson and McNaughton, 1994),
we performed template matching for several evenly
spaced compression factors: no-compression, 4x, 6x,
8x, and 10x. The sleep sessions during which the original approach template had significantly larger number
of matches (ps < 0.001) relative to shuffled distribution
was considered significantly matching. The 10x compression produced the lowest (12/24, 50%), while the
compression factor with largest proportion of significantly matching sleep epochs was 4x (19/24, 79%).
For the HF amplitude template the lowest proportion
of significantly matching sleep epochs was found
with no-compression (20/24, 83%), while the largest
proportion was found with 4x and 10x compression
(23/24, 96%). Overall, the proportion of significantly
matching sleep sessions was higher for HF amplitude
behavioral templates.
In order to test the hypothesis that modular
template matching during sleep is a part of memory
consolidation process and does not simply reflect the
reverberation of certain activity patterns during sleep,
we compared the match percentages for ‘no compression’ and range of equally spaced compression factors
(4-10x), between the pre-task-sleep and post-tasksleep. For the MUA templates, post-task sleep had
significantly higher template matching percentage
(percentage of time bins with a match that exceeded
Z-score value of 3) than pre-task sleep (Fig. 5A; main
effect of sleep session, F(1, 11) = 11.34, p < 0.01) and this
effect varied across compression factors (sleep session x compression factor interaction; F(4, 44) = 11.18,
p < 0.0001). In addition, there was a main effect of
compression factor (F(4, 44) = 6.97, p < 0.001). Planned
comparisons were conducted for pre- versus posttask-sleep for each compression factor. Post-task-sleep
had significantly more template matches for 6x, 8x,
and 10x (Fs(1, 22) > 4.73,, p < 0.05) but not 4x (Fs(1, 22) =
3.98, p = 0.058) compression factors, while pre- and
post- task-sleep match percentage did not differ for
‘no-compression’ (F(1, 22) = 0.75, p = 0.40).
Similar to MUA-based reactivation measures,
HF amplitude post-task sleep had significantly higher
template matching than pre-task-sleep (Fig. S2; main
effect of sleep session, F(1, 11) = 21.51, p < 0.001) and
this effect varied across compression factors (sleep
Laminar organization of encoding and memory reactivation in the parietal cortex
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
*
0.4
0.2
nc
C.
0.6
Match Percentage
*
4x
6x
8x
Compression Factor
nc
*
B.
2
Pre-Task
Sleep
20
D.
Task
4x Compression
Post-Task
Sleep
1.0
40
Time (min)
60
80
nc
0.8
0.6
0.4
Pre-Task
Sleep
Task
Post-Task
Sleep
1
10x
4x
sleep for 4x – 10x compression factors (Fs(1, 22) > 5.87,
ps > 0.05).
As illustrated for the MUA template example in
Figure 5B, for ‘no-compression’, there is a high match
density during the task, and low match density in posttask-sleep. A dramatically different pattern is shown
for 4x compression, where the match percentage was
much higher in post-task-sleep, relative to both pre-
No Compression
Match Percentage
Pre-Task-Sleep
Post-Task-Sleep
0.6
Normalized Match Percentage
A.
Match Percentage (Mean±SEM)
session x compression factor interaction; F(4, 44) = 9.20,
p < 0.001). In addition, there was a main effect of compression factor (F(4, 44) = 21.51, p < 0.0001). Planned
comparisons for pre- versus post- task-sleep for each
compression factor showed that for ‘no-compression’
pre-task-sleep did not significantly differ from posttask-sleep (F(1, 22) = 1.47, p = 0.24), while post-tasksleep had significantly more matches than pre-task-
20
4x
40
Time (min)
60
80
Rat 1
Rat 2
Rat 3
0.4
0.2
0.2
Pre- Task PostTask
Task
-Sleep
-Sleep
Pre- Task PostTask
Task
-Sleep
-Sleep
Pre-Task-Sleep Post-Task-Sleep Pre-Task-Sleep Post-Task-Sleep
Figure 5. Modular sequence reactivation occurs in parietal cortex and is compressed. See also Figure S2.
A. Mean (±SEM) match percentage (number of matches/number of time bins) across the compression factors for multiunit activity (MUA,) templates for slow-wave sleep periods. Template matching increases between pre- (blue) and posttask-sleep (red) for compressed data, but not for ‘no-compression’. For MUA and HF amplitude templates, reactivation
measures peak at 4x compression.
B. Example of MUA showing the match percentage (2 min bins) for ‘no-compression’ (no-compression, left) and 4x compression (right), over the pre-task-sleep, task and post-task-sleep. For ‘no-compression’, there is a high match percentage
during the task, and very low in post-task-sleep. For the 4x compression (right), match percentage is much higher in posttask-sleep, relative to both pre-task-sleep and task. Only the SWS periods from each sleep epoch are shown.
C. Mean of the pre-task-sleep, task and post-task-sleep as was calculated for each session and averaged across time
bins that are shown in B. Then a mean for all sessions was calculated for each rat. Finally, the mean (±SEM) of the rat
mean data is shows that across rats reactivation is stronger in post-task rest when a 4x compression factor is applied, but
not for ‘no compression’. To avoid the possible contribution of awake reactivation to template matching during task, only
the contiguous movement periods (>5 cm/sec) longer than 2 sec were used in quantifying the template matching during
task.
D. Normalized match percentage (number of matches / number of time bins divided by the peak value for that session)
across the ‘no-compression’ (left) and 4x (right) compression factors for multi-unit activity (MUA) templates for slow-wave
sleep periods for each session for each of 3 rats. Reactivation consistently increases between pre- and post-task-sleep for
compressed data, but not for ‘no compression’. For MUA and HF amplitude templates, reactivation measures peak at 4x
compression. Datapoints are normalized to pre-task-sleep values. * p<0.05.
Laminar organization of encoding and memory reactivation in the parietal cortex
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
0.4
Pre-Task-Sleep
Post-Task-Sleep
0.2
0
-0.2
−1
−0.5
0
0.5
1
Time Relative To Ripple Peak (s)
ual data sets (Fig. 5B) and the rat means (Fig. 5C), but
also when the pre-versus post task rest normalized (to
post-task rest) was plotted for ‘no-compression’ versus
4x compression data for each session for each rat (Fig.
5D). Nearly identical results were obtained for HF
amplitude (Fig. S2). Overall, MUA template – based
reactivation measures increased in post-sleep.
Parietal cortex reactivation is increased during hippocampal sharp wave ripples (SWRs). Finally, we
explored the role of hippocampal-cortical interactions
in modular memory reactivation by quantifying the
strength of parietal cortex template matches as a function of SWR events in the hippocampus. We found
that the strength of the SWR-triggered template match
was greater during post-task sleep for MUA based
templates (Fig. 6A), and also for HF amplitude based
templates (Fig. S3). For both MUA and HF amplitude
templates, the peri-SWR reactivation strength (maximum averaged ripple-triggered Z-value within +/- 500
ms peri-SWR time-window) was significantly greater
during post-task-sleep for (Fig. 6B; main effect of rest
Ripple-Triggered Average Z-peak (Mean±SEM)
Ripple-Triggered Mean Z-score
task-sleep and task. Next, the mean of the pre-task and
post-task-sleep was calculated for each session and averaged across the time bins shown in Figure 5B. Then
a mean for all sleep and task sessions was calculated
for each rat, for all the data like the example shown in
Figure 5B. To avoid the possible contribution of awake
reactivation to template matching during task, only
the contiguous movement periods (>5 cm/s) longer
than 2 s were used in quantifying the template matching during task. This showed that reactivation strength
for pre- vs post- task-sleep varied dramatically across
compression factor (‘no compression’ & 4x compression; Fig. 5C) as a function of task phase. First, posttask-sleep was greater than pre-task-sleep but only for
4x compressed data. Second, the matches where much
higher during the task phase for ‘no compression’ data
than for 4x compression data, reflecting the fact that
the templates do match well to the behavior session
that was used to generate them. Stronger reactivation
during post-task rest was not just observed for the session means (Fig. 5A), individual time bins for individ-
0.4
*
0.2
nc
*
*
4x
6x
8x
10x
Compression Factor
Figure 6. Modular sequence reactivation in parietal cortex is enhanced around sharp-wave ripples (SWRs) in the
hippocampus. See also Figure S3.
Left. Example of sharp-wave ripple (SWR) triggered average Z-scores for pre-task-sleep (blue) and post-task-sleep (red)
with 4x compression. SWR triggered peak amplitudes for multi-unit activity (MUA) templates, decaying to baseline within
300-400ms after ripple peak. Right. Ripple-triggered average peak amplitudes across the compression factors for MUA
templates. Peak amplitudes varied significantly across compression (F(4, 44) = 3.01, p < 0.05). Only slow-wave sleep periods were included in the analysis. * p<0.05.
Laminar organization of encoding and memory reactivation in the parietal cortex
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Pre-Task-Sleep
Post-Task2-Sleep
SWS duration (s)
1850 +/- 647
2484 +/- 349
REM duration (s)
141 +/-37
200 +/- 226
0.09 +/- 0.08
0.09 +/- 0.11
REM/SWS ratio
Table 2. Sleep architecture metrics for pre- versus post- task-sleep. For the REM duration and REM/SWS ratio, there
were not significant differences in pre- versus post- task-sleep (ts(11)<0.98, ps>0.35). However, SWS duration was significantly greater in post-task-sleep (SWS duration: t(11)=2.23, p<0.05).
session; Fs(1, 11) > 16.69, ps < 0.01), and the significant
effect of rest session varied across compression factors
(Fs(4, 44) > 3.01, ps < 0.05). The only difference between
measures was that there was a significant main effect
of compression factor for HF amplitude templates
(Fig. S3B; F(4, 44) = 18.87, p < 0.001), but not MUA
templates (F(4,44)=3.01, p = 0.25). To follow-up on the
significant interaction for MUA and HF amplitude
data, planned comparisons for pre- versus post- task
rest were conducted for each compression factor. The
ripple-triggered Z-score amplitude was significantly
larger for all compressed data (4x-10x; Fs(1, 22) > 4.77,
ps < 0.05), but not for ‘no-compression’ (Fs(1, 22)<1.97,
ps>0.17). Thus, for both MUA and HF signal templates the amplitude of ripple-triggered Z-value was
significantly larger for post-task-sleep particularly for
intermediate compression factors.
Sleep parameters. To assess the possible differences in
sleep architecture between pre-task-sleep and posttask-sleep we compared the durations of different
sleep states (SWS, REM), as well as their ratios across
these sleep epochs. There was not a significant effect
of sleep epoch on the duration of REM sleep or REM/
SWS ratio (ts(11)<0.98, ps>0.35; Table 2). SWS duration
was significantly greater in post-task-sleep (ts(11)>2.23,
p<0.05); however, match percentage reflects the number of matches/time unit so more slow-wave sleep will
not impact this measure.
DISCUSSION
We have shown modular coding of movement
in parietal cortex. Further, the coding is consistent
across a range of cortical depths, strengthening the
idea that this meso-scale coding is organized in a
potentially modular fashion. In addition, we found
that there are interactions between modules during
post-task-sleep as measured by templates reflecting
the task segments leading up to the reward zone.
These modular memory reactivation events suggest
that inter-modular interactions are involved in normal
learning and memory. Finally, we demonstrated that
the high frequency LFP amplitude signal from a given
tetrode produces a result that is highly similar to the
multi-unit activity for both modular motion tuning
and memory reactivation.
Single unit recording studies and behavioral
studies have pointed towards an egocentric reference
frame in parietal cortex. However, this is the first
demonstration of mesoscale functional encoding from
a local population of single cells organized around a
specific motion preference. For example, coordinate
transformation networks may exist embedded within a
functional module for executing the appropriate action
sequence (Byrne et al., 2007; McNaughton et al., 1995;
Wilber et al., 2014; Xing and Andersen, 2000). In fact,
the motion modules could possibly even serve as a
common output reference frame to a brain network for
decision related computations involving parietal cortex
(Goard et al., 2016; Hanks et al., 2015; Harvey et al.,
2012; Licata et al., 2016). In other words this modular movement based encoding may be a fundamental
coding framework in parietal cortex. This common
tuning, likely reflecting high intrinsic connectivity
among the single units (i.e., a module; Buxhoeveden
and Casanova, 2002) suggestive of a cell assembly.
Such a functional module may be precisely the effector
Laminar organization of encoding and memory reactivation in the parietal cortex
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
unit theorized to be critical for sufficient size to trigger responses in target areas (Breitenberg and Schulz.,
1991; Bush and Sejnowski, 1994; Gabbott et al., 1987)
either at the column (Mountcastle, 1997; Szentagothai,
1975) or possibly even macrocolumn level (Mountcastle, 2003).
It should be noted that we did not examine
all known encoding modes in parietal cortex. One
encoding framework that seems likely to exist at the
modular level is route-centered encoding, described
in detail at the single unit level (Nitz, 2009; Nitz, 2006,
2012). Unfortunately, the task we employed here is not
optimal for detecting route-centered modulation. In
contrast to our previous single unit analysis that suggested consistent head direction tuning across depth,
we did not find evidence for MUA tuning to head
direction (Chen et al., 1994a; Wilber et al., 2014). It is
possible the lack of head direction tuning for MUA but
presence with single cells means that the head direction tuned cortical columns are present but are very
narrow (e.g., microcolumn) and thus not detected with
MUA. In addition to head direction encoding other
types of world-centered (allocentric) encoding have
been reported in parietal cortex that could exist at the
modular level but were not assessed here (Nakamura, 1999). Similarly, outside of parietal cortex, there
are numerous examples of local cell populations that
share functional similarities, including primary visual
areas, and the barrel cortex (Petersen, 2007; Roudi et
al., 2015; Sincich and Horton, 2005). Local populations
with functional similarity also exist in higher cortical
areas, for example, grid cells in the medial entorhinal
cortex (Moser et al., 2014; Roudi et al., 2015) or object
feature columns in inferior temporal cortex (IT; Tsunoda et al., 2001). Presumably the current approach
can be applied to further understanding of many of
these brain systems.
We demonstrated modular reactivation in
parietal cortex at the ensemble levels, using template
matching. This method was previously applied in
memory reactivation studies of ensembles of isolated
single neurons (Euston et al., 2007; Louie and Wilson,
2001b; Pavlides and Winson, 1989; Skaggs and McNaughton, 1996; Tatsuno et al., 2006; Wilson and McNaughton, 1994). Although the resemblance between
the behavioral and sleep activity patterns could be due
to simple re-occurrence of the similar patterns across
the sleep-wake cycle, multiple lines of evidence sup-
port the notion of modular memory reactivation in the
present study. First, the behavioral template matching
was more prevalent during post-task, relative to pretask sleep, an effect present after normalizing for the
SWS duration in each sleep epoch (Fig. 5). This suggests that the difference in probability of certain brain
activity patterns between pre- and post-task-sleep is
affected by intervening experience, one of the critical
requirements for the detection of memory-related
processes. Second, consistent with numerous reports
of memory reactivation occurring at the temporally
compressed timescale (Euston et al., 2007; Nadasdy et
al., 1999; Peyrache et al., 2009; Wilson and McNaughton, 1994), we observed increased matching during
post-task-sleep for the range of compression factors
(4-10x) while no significant change was observed for
non-compressed patterns. Temporal compression
during memory consolidation is postulated to create
the optimal conditions for spike-timing dependent
plasticity (Lansink and Pennartz, 2014; Markram et al.,
1997). Finally, temporal coupling of modular reactivation in parietal cortex with SWRs suggests that, similar
to reactivation in medial pre-frontal cortex (mPFC;
Euston et al., 2007; Peyrache et al., 2009), this process
is part of hippocampo-cortical communication during
sleep, one of the hallmarks of memory consolidation
(Maingret et al., 2016).
We found a certain degree of significant matching with pre-task-sleep activity, consistent with previous reports (Dragoi and Tonegawa, 2011; Peyrache
et al., 2009). This is consistent with the idea that the
information incoming during experience modifies the
pre-existing patterns (Luczak et al., 2009), but does not
completely perturb them. The reactivated patterns resemble the templates constructed based on the average
activity during approach to reward locations randomly
assigned on each trial, and therefore likely encode the
behavioral sequence reinforced by subsequent reward.
Reactivation of behavioral pattern averaged over many
different spatial trajectories, containing a range of
head direction, velocity and other tuning properties of
parietal cortex, supports the abstraction role of memory consolidation (Lewis and Durrant, 2011; Tse et al.,
2007), as the regular aspects of episodic memory are
extracted into the semantic domain.
Finally, we showed that the HF-LFP signal has
nearly identical reactivation dynamics compared to dynamics observed with MUA activity. Although the ex-
Laminar organization of encoding and memory reactivation in the parietal cortex
bioRxiv preprint first posted online Feb. 25, 2017; doi: http://dx.doi.org/10.1101/110684. The copyright holder for this preprint (which was not
peer-reviewed) is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
act contribution of LFP generation mechanisms to different frequency ranges are a matter of debate (Buzsáki
et al., 2012), correlation between the HF amplitude
and MUA activity is highly correlated with the level of
spiking activity recorded at the individual electrode,
which suggests that the instances of low correlation between MUA and HF amplitude are due to spike subsampling on the individual electrode. Another factor that
could decrease the MUA-HF amplitude correlation is
that the spiking contribution to HF amplitude depends
on the individual neuron type and recent spiking history, as during bursting activity, where the amplitude
of subsequent spikes tend to decrease. Finally, detection of spikes that comprise MUA clusters were thresholded and thus these clusters represent spiking activity
within a more limited spatial range than HF amplitude
which is not thresholded and thus could potentially
detect more distant spiking activity.
Consistent with numerous reports describing
mostly cortical-hippocampal interactions (notably
mostly the pre-frontal cortex), we found enhanced reactivation of behavioral templates simultaneously with
SWRs in the CA1 field of the hippocampus (Jung et al.,
1998; Peyrache et al., 2009; Peyrache et al., 2015; Siapas
and Wilson, 1998; Singer and Frank, 2009; Wierzynski
et al., 2009). Though a recent report found that equal
numbers of prefrontal cortex single cells were excited
or inhibited during hippocampal SWRs; while other single cells were not modulated by the SWR at all
(Peyrache et al., 2009). This suggests that the modular
re-activation we observed may represent a subset of
the single cells in that cortical region.
In summary, we have shown modular self-motion tuning in rat parietal cortex. These motion modules also participate in memory re-activation as measured by templates constructed from the pre-reward
task phase. Our findings suggest a potential fundamental level of organization in the rat in parietal cortex and
open up a new avenue for bridging the gap between
macro-level measures of brain activation in humans
and unit recording studies in non-human animals.
Author Contributions
AAW and BLM designed the experiment; AAW
conducted the experiment; AAW and IS analyzed the
data; AAW, IS and BML wrote the paper.
Acknowledgements
We thank Mikko Oijala, Dr. David Euston, Dr. Michael Eckert, and Dr. Masami Tatsuno for technical
assistance with analyses and Scott Killianski and
Christin Montz for thoughtful comments as we constructed analyses. Alberta Innovates Health Solutions
Polaris Award to BLM. AG049090, MH4682316, and
an Alberta Innovates Health Solutions Fellowship to
AAW.
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